PLS path modelling has previously been found to be robust to multicollinearity both between latent variables and between manifest variables of a common latent variable (see e.g. Cassel et al. (1999), Kristensen, Eskildsen (2005), Westlund et al. (2008)). However, most of the studies investigate models with relatively few variables and very simple dependence structures compared to the models that are often estimated in practical settings. A recent study by Nielsen et al. (2009) found that when model structure is more complex, PLS path modelling is not as robust to multicollinearity between latent variables as previously assumed. A difference in the standard error of path coefficients of as much as 83% was found between moderate and severe levels of multicollinearity. Large differences were found not only for large path coefficients, but also for small path coefficients and in some cases the difference could lead to a change in conclusions about variable importance